Karhunen-Loeve Transform and Sparse Representation Based Plant Leaf Disease Recognition

To improve the classification accuracy rate of apple leaf disease images and solve the problem of dimension redundancy in feature extraction, Karhunen-Loeve (K-L) transform and sparse representation are applied to apple leaf disease recognition. Firstly 9 color features and 8 texture features of disease leaf images are extracted and taken as feature vectors after dimensionality reduction by the K-L transform. Then, for each of apple mosaic virus, apple rust and apple alternaria leaf spot, 40 apple leaf images are selected as the training samples, whose feature vectors are made up of the dictionary of the sparse representation, respectively. Each testing sample is classified into the class with the minimal residual. The identifying results using the proposed method are analyzed and compared with those of the Support Vector Machine (SVM) and original sparse representation method. The average classification accuracy rate of the proposed method is 94.18%, which confirms its good robustness. In addition, the proposed method not only improves the plant leaf disease classification accuracy but also solves the redundancy problem of the extracted features.